Identification of particles in fluid

ABSTRACT

A method for the identification of unknown particles contained in a fluid. The method utilizes a source of radiation and at least one radiation detector to measure the radiation scattered by an unknown particle in the fluid. The measurement for the unknown particle is compared with a standard radiation scattering pattern capable of uniquely identifying a previously identified particle and the unknown particle is identified based upon the comparison.

BACKGROUND OF THE INVENTION

[0001] There has been a longstanding need for techniques to detect andidentify unknown particles contained in fluid media. One example of sucha need is the desire to detect and identify pathogenic microorganismscontained in water. Protozoan parasites such as Cryptosporidium parvumand Giardia lamblia have been recognized as important waterborneetiologic agents of disease after contact with or ingestion ofcontaminated water. C. parvum is of major concern because it exhibitshigh resistance to disinfectants at the doses routinely applied in watertreatment plants, has a low infectious dose, and no drug is currentlyapproved for prophylaxis or therapy.

[0002] Current water quality monitoring techniques for Cryptosporidiumand Giardia have well-known and serious limitations. First, standardtechniques—from sample collection to final identification andenumeration—can take at least a day. This delay reduces or eliminateshealth benefits associated with monitoring (M. J. Allen et al., JAWWA,September 2000). Second, these techniques are labor intensive andexpensive. Third, samples are often collected discretely; so transientcontamination spikes are very likely to be missed by sporadic sampling.Fourth, the accuracy of the identification techniques is unacceptablypoor. For example, typical recovery and identification for two standardmethods, immuno-fluorescence assay (IFA) and flow cytometry cell sorting(FCCS), is around 40% for Giardia and around 40-50% for Cryptosporidium,with high coefficients of variation and high false positive rates,primarily from benign species such as algae (Comparative Health EffectsAssessments of Drinking Water Technologies: Report to Congress, November1988; M. LeChevallier, JAWWA, September 1995, p. 54; M. Frey, C.Hancock, and G. S. Jackson, AWWARF and AWWA, 1997; J. L. Clancy et al.,JAWWA, September 1999).

[0003] Attempts to monitor water for the presence and identity ofmicroorganisms by light scattering have met with little success—thedifficulty lies in the ability to “invert” the light scattering data todetermine what particle did the scattering. The inverse scatteringproblem is well known in classical electromagnetic theory. Unlike the“forward scattering” problem, in which the scattered radiation iscompletely predictable based on sufficient information about thescattering particle, the inverse scattering problem is defined byattempting to determine the physical properties of the scatteringparticle from the scattered radiation. Such physical properties include,for example, size, shape, internal structure, and refractive index.

[0004] A well-known solution to the inverse scattering problem isInverse Synthetic Aperture RADAR. RADAR waves are scattered from amoving target that changes its attitude relative to the RADAR source.Scattered phase and amplitude information is collected, and a RADARimage of the target is reconstructed using signal-processing techniques(c.f. E. F. Knott, J. F. Shaeffer, and M. T. Tuley, Radar Cross Section,Artech House, Inc., Norwood, Mass., 1985. P. 202).

[0005] The analogous problem in optics is more problematic, becausephase information is difficult to obtain due to the short wavelengthsinvolved. Without phase information, a rigorous analyticalreconstruction of the scattering particle, particularly a complexobject, such as a microorganism, becomes untenable using standardtechniques.

[0006] Quist and Wyatt achieved a solution to the optical inversescattering problem using scattered amplitudes alone in the early 1980's(G. M. Quist and P. J. Wyatt, J. Optical Soc. Am., November 1985,pp.1979-1985; U.S. Pat. No. 4,548,500). Because this technique reliesupon simultaneous measurement of various scattered light angles, thetechnique is called the Multi-Angle Light Scattering (MALS) technique.Using a scheme called “strip maps,” Quist and Wyatt demonstrated that itis possible to uniquely and rapidly characterize simple particles, suchas homogeneous and isotropic spheres, homogeneous rods, and homogeneousellipsoids, using optical data generated solely from the differentialcross section (the angular dependence of the scattering amplitude)without explicit phase information. However, the strip map technique islimited to simple geometric structures.

[0007] The MALS technique has been utilized with various microparticles,including, bacteria and flyash, to produce coherent scattered lightpatterns with multiple nulls. In 1989, Wyatt and Jackson extended theMALS technique to classifying microbiological particles in water (P. J.Wyatt and C. Jackson, Limnology and Oceanography, January 1989, pp.96-112). They demonstrated that it is possible to classify 12 distinctspecies of phytoplankton in seawater with a statistical confidence levelof greater than 99%.

[0008] The problem of waterborne outbreaks of disease related to Giardiaand Cryptosporidium, and their presence at the effluent ofstate-of-the-art water treatment plants complying with currentregulations, clearly indicates the importance of effective real time,continuous monitoring systems to identify their presence in water. Thus,it is desirable to develop a method to identify particles in a fluid,with one example being the use of such a method to detect and identifyrapidly and accurately Cryptosporidium and Giardia in drinking water.

DISCLOSURE OF THE INVENTION

[0009] The present invention provides a method for the identification ofparticles in a fluid. More particularly, the invention provides a methodfor the identification of unknown particles contained in a fluidcomprising: a source of radiation and at least one detection means todetect said radiation secured in a predetermined position relative tothe radiation source, positioned to sample a fluid. The fluid isinterrogated by the source of radiation, and the radiation scattered byan unknown particle in the fluid is measured at the detection means.Then, the results obtained in the measurement step are compared withstandard results previously obtained from an identified particle,wherein the standard results are obtained by generating a radiationscattering pattern capable of uniquely identifying the previouslyidentified particle, and the unknown particle is identified based uponthe results of the comparison step.

[0010] In one aspect, the present invention provides a new detection andenumeration system for use in on-line, continuous, and real timemonitoring of fluids for microorganisms. In this embodiment, the presentinvention employs a technique called multi-angle light scattering,utilized with a source of electromagnetic radiation, to provide, forexample, the ability to continuously monitor the quality of drinkingwater. In tests performed with low turbidity water, it is found that thelight scattering patterns of Cryptosporidium, Giardia, and a backgroundinterference organism such as algae are sufficiently unique todiscriminate between the pathogens and the background. These resultsdemonstrate that a pathogen monitoring system can be provided that iscontinuous, real-time, and on-line. Such a system provides a publichealth benefit by providing timely information to operators of treatmentplants, reservoirs and distribution systems and may provide an economicbenefit by enabling treatment plant operators to optimize disinfectionand treatment processes.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011]FIG. 1 presents a schematic block diagram of the major componentsof one embodiment of the present invention;

[0012]FIG. 2 presents a schematic diagram of one embodiment of adetection means to detect radiation scattered by a particle inaccordance with the present invention, in which

[0013]FIG. 2B depicts a single detector,

[0014]FIG. 2C depicts the interrogation of a spherical particle,

[0015]FIG. 2D depicts the interrogation of a “pear” shaped particle,

[0016]FIG. 2E depicts the interrogation of an “ovoid” shaped particle,

[0017]FIG. 2F is an isometric depiction of one embodiment of anapparatus according to FIG. 2,

[0018]FIG. 2G depicts data memory obtained from the interrogation of anumber of particles, and

[0019]FIG. 2H graphically depicts the data expected to be obtained bythe interrogation of a spherical particle correlated to the angle of thedetector to the radiation beam, and

[0020]FIG. 3 presents a schematic block diagram of the data flow for thegeneration of a radiation scattering pattern in one embodiment of thepresent invention;

[0021]FIG. 4 presents a schematic block diagram of the collection ofdata for the generation of a radiation scattering pattern in oneembodiment of the present invention

[0022]FIG. 5 presents a graphic representation of scattering dataobtained using algae in the embodiment of FIG. 2;

[0023]FIG. 6 presents a graphic representation of scattering dataobtained using Cryptosporidium parvum in the embodiment of FIG. 2;

[0024]FIG. 7 presents a graphic representation of scattering dataobtained using Giardia lamblia in the embodiment of FIG. 2;

[0025]FIG. 8 presents a two dimensional projection of a radiationscattering pattern provided in accordance with the present invention;and

[0026]FIG. 9 presents a two dimensional projection of a refinedradiation scattering pattern provided in accordance with the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

[0027] The present invention provides a method for the identification ofparticles in a fluid. More particularly, the invention provides a methodfor the identification of unknown particles contained in a fluidcomprising a source of radiation and at least one detection means todetect said radiation secured in a predetermined position relative tothe radiation source, positioned to sample a fluid. The fluid isinterrogated by the source of radiation, and the radiation scattered byan unknown particle in the fluid is measured at the detection means.Then, the results obtained in the measurement step are compared withstandard results previously obtained from an identified particle of thesame type, wherein the standard results are obtained by generating aradiation scattering pattern capable of uniquely identifying thepreviously identified particles, and the unknown particle is identifiedbased upon the results of the comparison step.

[0028] Unless otherwise indicated, the following terms will have thefollowing meanings:

[0029] The term “radiation” will be taken in its broadest sense toinclude any form of energy or particle transmitted from its source by asurrounding medium. Most commonly, but without limitation, the forms ofradiation found useful in the present invention will includeelectromagnetic radiation, such as light and microwaves, radioactiveemissions, such as α, β and γ emissions, and acoustic radiation, such assound waves. The particular form of radiation selected will depend, atleast in part, on the nature of the particle that is sought to bedetected and identified.

[0030] The term “detect” will be taken in its broadest sense to includeany means to sense the presence of the thing sought. In the presentinvention, a detection device will commonly include the ability torespond to and measure the radiation from the scattering source.

[0031] The term “radiation scattering pattern” will be taken in itsbroadest sense to include any set of data that is generated as a resultof detecting the radiation scattered by a particle of interest when theparticle is subjected to the influence of a source of radiation.

[0032] The term “identify” will be taken in its broadest sense toinclude any means of detecting and classifying a particular particle andassigning it to a specific particle type, and uniquely distinguishing itfrom particles of a different type. In the case of microorganisms, itmay be sufficient to classify the particles by genus, by genus andspecies, by genus, species and strain, or some alternative form ofclassification.

[0033] The term “interrogate” will be taken in its broadest sense toinclude any means by which a fluid that may contain a particle ofinterest is subjected to the influence of a source of radiation.

[0034] The term “measure” will be taken in its broadest sense to includeany quantification of the existence or magnitude of the detectedradiation.

[0035] The term “fluid” will be taken in its broadest sense to includeany medium having the property of flowing, including both gaseous andliquid media.

[0036] The term “particle” will be taken in its broadest sense toinclude any object of matter of sufficient size to be capable of beingdistinguished from the fluid medium. Particles will typically be amember of a particular type of particle, capable of being distinguishedfrom particles of other types. In certain embodiments of the presentinvention, the particles will be biological, such as microorganisms, andeach type of microorganism will typically constitute a separate species,or an identifiable strain of a species.

[0037] The term “algorithm” will be taken in its broadest sense toinclude any clearly specified process for computation, that is, a set ofrules that, if followed, will give a prescribed result. Examples ofalgorithms include, without limitation, multiple analysis of variances(MANOVA), neural networks, simulated annealing, algorithm-independentmachine learning, fuzzy logic, grammatical methods, and other techniquesfor pattern recognition.

[0038] In one aspect, embodiments of the present invention provide a newdetection and enumeration system for use in on-line, continuous, andreal time monitoring of fluids, such as water, for the presence ofparticles, such as specific microorganisms. In this aspect, forembodiments involving the use of electromagnetic radiation to detectparticles in liquids, the present invention employs a technique calledmulti-angle light scattering (MALS), utilized with a source ofelectromagnetic radiation, to provide, for example, drinking waterquality monitoring. In tests performed with low turbidity water, it isfound that the radiation scattering patterns (RSPs) of Cryptosporidium,Giardia, and various species of algae commonly misidentified asCryptosporidium in standard tests, are sufficiently unique todiscriminate between the pathogens and the background algae particles.These results demonstrate that a pathogen monitoring system can beprovided that is continuous, real-time, and on-line. Such a systemprovides a public health benefit and can enable treatment plantoperators to optimize disinfection and other treatment processes,thereby saving treatment costs.

[0039] In such embodiments, a device useful for the gathering of datacan be constructed in accordance with teachings well known in the art.For example, a device useful in the practice of the present inventioncan be constructed generally in accordance with U.S. Pat. No. 4,548,500,the relevant portions of which are incorporated herein by thisreference.

[0040] Additional devices that can be adapted to the practice of thepresent invention include devices disclosed in U.S. Pat. Nos. 5,125,737and 5,808,738, the relevant portions of which are incorporated herein bythis reference. Similar such devices can also be obtained commercially,for example, as the DAWN Model B MALS Measurement System available fromWyatt Technology Corporation, Santa Barbara, Calif.

[0041] The present invention will now be described hereinbelow, and withreference to the drawings, in which FIG. 1 shows a schematic blockdiagram of the major components 10 of an embodiment of the presentinvention that utilizes electromagnetic radiation and the MALStechnique. Referring to the FIG. 1, a pipe 12 containing a stream ofwater to be sampled by the present system is depicted, together with thesampling point 14 where water is diverted from out of the stream. Waterprovided by sampling point 14 can be provided under pressure from thestream of water in the pipe 12, or can require pumping. In addition,sampling point 14 can extend into the pipe 12 to sample from parts ofthe stream other than the wall region. A portion of the detectingapparatus 16 (colloquially termed the “read head”) can be positionedexternal to pipe 12, or can be submerged within pipe 12 directly intothe water stream. One embodiment provides that the system be maintainedexternal to the water and measuring a “side stream” of water. Water thatis sampled by the detecting apparatus 16 typically constitutes only asmall portion of the water that is passing by the sampling point 14, andmay optionally not be returned to the original stream.

[0042] The detecting apparatus 16 depicted in FIG. 1 provides a sourceof electromagnetic radiation 17, for example a coherent light source,such as a laser, to provide illumination and interrogation of the fluid(see FIG. 2). In certain embodiments, the light source is a single laserat a fixed wavelength, but in general, one can utilize multiple lightsources, or a single light source with several discrete (or continuous)wavelengths. As shown in FIG. 2, and described in FIG. 4, particlessuspended in fluid pass through the scattering region (or “detect zone”)18 in detecting apparatus 16. Light which encounters a particle 20suspended in the fluid is scattered 22 in a manner different than thelight transmitted through the surrounding fluid medium, and a portion ofthe scattered light is directed into discrete (or continuous) detectorsets 24 which measure the intensity of the scattered light.

[0043] The electro-optical detectors 24, which generate analog opticalsignals, collect the scattered light and these signals are thenamplified to a useful level. These signals are either processed by localelectronics, or sent via a transmission means 28 (FIG. 1) to a local orremote processor, such as a personal computer, or workstation 30 (FIG.1). The information can be sent via wired or wireless communications,and can be processed by a base station computer, or can be sent viaother means (e.g. a network of linked digital computers) to a remoteprocessor.

[0044] Real-time information regarding the status of the water passingthrough the present detection system can then be provided to a userinterface, and displayed for observation by a user of the system.Alternatively, the system can provide warning signals, e.g. optical,electronic, or acoustic signals, to a local or remote user, indicatingthat there is a detection of a particle or several detections of thesame particle.

[0045] Processing of Information from the System.

[0046] The present invention is generally, though not exclusively, usedin two forms: First, it is used in the generation of RSPs of a desired“target” microorganism, such as Cryptosporidium parvum. There is nopractical limit to the type of particle or variety of particles that canbe identified using this system. The performance of the system scaleswith wavelength and the size of the particle, and there is no intrinsicneed to limit this technique to light as a form of electromagneticradiation, nor to electromagnetic radiation as a form of radiation.Other forms of radiation, such as radioactive emissions, and acousticradiation, such as SONAR, can be employed in the practice of theinvention.

[0047] Second, once the RSPs are generated, the invention is used todetect and identify the particles whose RSPs are stored in memory,either in the local electronics, or in the supported computers, such asthose discussed previously.

[0048] Generation of Radiation Scattering Patterns

[0049]FIG. 3 shows a schematic diagram of the data flow through aspecific embodiment of the system for the generation of radiationscattering patterns (RSPs). A sample of the target microorganism isintroduced into a fixture that contains the read head (the radiationsource and the detection apparatus). Raw data is collected from the readhead by a commercial data acquisition program such as LabView (byNational Instruments), operating on computer 30 (FIG. 1). The raw datais generated from the optical signals as described above and representedin FIG. 4.

[0050] An analog to digital (A/D) circuit translates the analogelectrical signals into digital signals that are then stored by thecomputer. Sixteen channels are typically used as a matter ofconvenience, as the 16-channel configuration is typical for A/D'savailable commercially, and in a representative embodiment of theinvention, 16 photodetectors (e.g. photomultiplier tubes or solid statedetectors such as photodiodes) are utilized for convenience. However,other configurations are easily adopted, i.e. a greater or lesser numbercan be used.

[0051] Typically, particles are only rarely passing through the laserbeam for test particle concentrations of approximately 200microorganisms/mL or less. The passage of each such particle is termedan “event.” The raw data collected by the system includes all channeldata amplitudes as a function of time for the duration of the testsession.

[0052] Referring again to FIGS. 3 and 4, data is collected and initiallyprocessed by subtracting out the background scatter for each channel.Then each channel is normalized through data taken prior to the testmeasurements on a solution of isotropic scattering particles, such asdextran, or small polystyrene latex spheres. The non-event data, whichgenerally occupies most of the time of a measurement session, is removedby a simple criterion, such as whether a single channel has a signal “n”times the AC noise level above zero (since the background issubtracted). The value n can be selected as desired in order to maximizethe benefit obtained from the application of the technique. Values of ngreater than 2 have been found useful.

[0053] Further analysis is conducted of the absolute and relativestrength of a signal in order for the signal to be consideredmeaningful. This further analysis precludes the consideration ofpartially illuminated oocysts, of foreign bodies too large or too small,or of optical and electronic artifacts.

[0054] The data recorded from all channels during an event are kept inmemory, and the non-event data are discarded. Events are then selectedfrom the stored data, based on a criterion that: (a) a trigger channel,chosen to be one of the channels, has a signal above a level of “m”times the AC noise level, and (b) several other channels also havecoincident signals above background during this same event time period.The value m can be selected as desired in order to maximize the benefitobtained from the application of this technique. Values of m greaterthan 2 have been found useful. In addition, there are various otherschemes that can be used.

[0055] Due to the intensity profile of the laser beam, each of thesignals generated by the electro-optical detector 24 (FIG. 1) resemblesa Gaussian bell curve. When all 16 data channels are displayedsimultaneously, a family of 16 bell curves appears for each scatteringevent. These bell curves differ mostly in their amplitude and to a minordegree in the relative position of their maximums Therefore a scatteringevent can be rather accurately described by the values of the amplitudemaximums alone.

[0056] Once an event is located, the maximum values of all channelsduring that event are stored. Thus, an event is described by a set of 16numbers. Hundreds to thousands of events captured in this manner arethen stored as representative of the sample being measured. The datacollected in this manner are then used to generate the RSP of themicroorganism under analysis.

[0057] Details of the Generation of the Radiation Scattering Pattern

[0058] The event data are represented by matrices of numbers. Eachmatrix row represents an event, and each column represents the signalamplitude as discussed. To establish specificity, one such matrix isproduced for each of particle types A, C, and G (algae, Cryptosporidium,and Giardia respectively). However, despite all practical precautionstaken, a certain percentage of contamination data in each matrix must beexpected. It is in the mutual correlation of the 16 matrix columns, andthe variation of this correlation from matrix A to C to G, where thedesired information is found.

[0059] The RSP is generated as follows (representative of numerous wayssuch a pattern can be established): First, measurements are made of allof the selected types of particles/microorganisms in the mannerdiscussed above. In addition, measurements are also made of otherparticles that are expected to provide interference to the measurements.Next, the logarithm of the data values for each event is taken. Then,all of these measurements are submitted to a pattern recognitionalgorithm, such as a multiple analysis of variances analysis (MANOVA),for at least a subset of the 16 data channels of the system. MANOVA is awell-known statistical technique that finds the optimum linearcombinations (the “canonical space”) of channels such that the data isoptimally grouped amongst like particle types and maximally separatedfrom distinct particle types in the MANOVA canonical space. In this way,the measurement data are subjected to an algorithm that enhances theseparation of data generated from measurements of the target particlesfrom data generated from measurements of distinct particles. Generally,application of MANOVA to the measurement data provides distinction butnot clear separation of the different particles types from each other.Therefore, further refinement of the RSP may be desirable.

[0060] In such a case, a numerical technique (colloquially called the“Wall”) is optionally applied in order to further enhance the separationof the measurement data generated from a particular particle type fromthe measurements from data generated from distinct particle types. The“Wall” is an erasure set technique that searches each of the points inthe MANOVA canonical space for all of the samples and requires that “N”nearest neighbors must be of the same type. If they are not, then thisdata point is eliminated from the developing RSP. If this criterion issatisfied, then the data point is retained and becomes part of the firstorder RSP of the target particle type. The value N can be selected asdesired in order to maximize the benefit obtained from the applicationof the technique. Values of N between 1 and 20 have been found useful.

[0061] Thereafter, a further criterion can be applied to refine theparticle type-specific RSP even further. For each point in a particletype RSP, the average distance in the MANOVA canonical space to allpoints of its like particle type is calculated, and if a point isgreater than “x” times the mean distance, it is rejected and theremainder of the points are taken to define the particle type-specificRSP. The value x can be selected as desired in order to maximize thebenefit obtained from the application of the technique. Values of xgreater than 1 have been found useful.

[0062] Particle Identification

[0063] Once specific RSPs have been developed for each of the typesparticles, then the measurements taken from unknown particles can becompared against the established RSPs as follows: First, the unknownparticle event is processed as described earlier. The logarithm of the16 channel values is taken. These values are then translated into theMANOVA canonical space by the linear transformation defined by theMANOVA that generated the RSPs to begin with. Once in MANOVA canonicalspace, the particle value point is tested against the establishedparticle type-specific RSPs by calculating the average distance betweenthe unknown point and “z” points of the RSP to which the unknownparticle is compared. The value z can be selected as desired in order tomaximize the benefit obtained from the application of the technique.Values of z between 1 and 20 have been found useful. If the averagedistance is less than “y” times the average for the RSP itself, then theparticle is identified positively with that particle type RSP. The valuey can be selected as desired in order to maximize the benefit obtainedfrom the application of the technique; values of y between 0.5 and 10have been found useful.

[0064] Details of the Analysis of the Signals.

[0065] The signal analysis from the light scattering amplitudesprimarily follows the classical procedure of MANOVA. This technique isfrequently used, for instance, for automatic pattern recognition.MANOVA's concept is the detection and separation of multiple modes inthe distributions of the data. The representation of one event composedof 16 channel data, i.e. one matrix row, can be thought of as a point ina 16 dimensional data space. Accordingly, an entire matrix of data canbe thought of as representing a data cloud in 16 dimensional (16D)space. That is, the density of such a cloud in 16D space is themulti-dimensional equivalent of a Gaussian distribution in 1 dimensionalspace. To the extent that one can recognize distinct data clouds,specificity can be established. In addition, by recognizing order, thedimensionality may be reduced without loss of specificity. MANOVA isfrequently capable of reducing the original number of dimensions to asmaller number without losing significant information about thespecificity of the data. In one embodiment of the present invention,MANOVA will reduce the data from 16D to 3D space without losinginformation about the origin of the data (i.e. the type of particle).

[0066] An algorithm such as MANOVA can be easily executed by computersusing pre-programmed software, for example, by using the well-knownsoftware MATLAB (The MathWorks) on personal computers. Each dimension isan optimally weighted composition of the original data. Therefore it isfurther possible to investigate the significance of information providedby a particular data channel. Frequently, several channels areredundant. Therefore, they can either be turned off or re-positioned soas to gather additional information.

[0067] In summary, with certain embodiments of the invention, theproblem to be solved is: Given an event, i.e. a matrix row of 16numbers, determine from what type of particle the event originated.First, the data clouds can be represented as very dense kernels close tothe center of the canonical data space and low density in the farfields. This highly unequal density makes the proper separation of datapoints difficult. Therefore, it is deemed important to find a means ofequalizing the density. This can be achieved by simply taking thelogarithm of the data and applying MANOVA to the transformed data.

[0068] Next, the problem of overlap of the three data clouds in thecanonical space can be overcome. This overlap is a result of similarevents stemming from different particle types and sub-optimal detectorplacement and therefore causes errors or uncertainties in theconclusions and assignment of particle identity. One representativetechnique in further refining the invention is to establish “Walls”between the data clouds in the MANOVA canonical space where all datapoints are simply removed. The “Walls” are established by demanding thata data point be surrounded by a substantial number of the same type inorder to be considered a valid point. This procedure produces a compactdata space for each type within which there is little doubt to what typea particular data point belongs. The “Walls,” however, reduce the yieldin the operation of the apparatus; i.e. when the invention decides thedecision is most likely correct and assigns an identity to an unknownparticle. At this point, in many cases the present invention would notassign any identity at all. However, it can be demonstrated thatrepetitions of the scattering measurements will decrease the risk that aparticle will escape detection. Such repetitions can be arranged inspace and/or in time. If the number of repetition of measurements for aparticular unknown particle is larger than a certain value, both theyield will increase and the error rate will decrease. The optimal numberof repetitions is a function of the yield achieved by a singlemeasurement.

DESCRIPTION OF A SELECTED EMBODIMENT

[0069] In order to explain certain aspects of the invention in greaterdetail, a description of the invention is provided for a simplifiedembodiment of the invention that involves the use of electromagneticradiation to detect and identify selected types of microorganisms insupplies of drinking water.

[0070] For example, FIG. 2 illustrates a system 100 for identifying suchparticles. The system includes a radiation source 102 which is, e.g. alaser that generates a thin collimated laser beam 104. In thisembodiment it is preferred to use electromagnetic radiation in a rangebetween and including ultraviolet to infrared, with the particular beam104 being linearly polarized light having a wavelength of approximately685 nm. The microorganism particles to be detected will have a diameterin the range of about one to thirty wavelengths (about 0.7 to 20microns). The beam projected by the radiation source is narrow, having awidth 106 of approximately 0.1 mm. A detector support framework 110supports a plurality of detectors 112 in a fixed relationship to theradiation source, with each detector oriented to detect light emittedfrom a limited detect zone 114 intersected by the laser beam 104. Eachdetector 112 is spaced about 60 mm from the detect zone 114. Thedetectors are located at different angles (e.g. A-D in FIG. 2) from theforward direction of travel of the laser beam 104. FIG. 2B shows one ofthe detectors 112, depicting how it detects light received through anarrow angle E (such as 2.5°) from the direction 122 in which thedetector is aimed. The detector includes an elongated narrow tube 130with an inside surface that absorbs red light. A photodetector 132 liesat the distal end of the tube. Light traveling along a path indicated at134 (outside the narrow detection angle) will strike the inside of thetube 130 and be absorbed there, so it will not impinge on the photodetector 132; only light within the narrow angle E will be detected bythe photo detector 132. It is also noted that a polarizing filter 136lies over the end of the tube, to admit only light polarized in acertain direction, for example a vertical direction.

[0071] Referring again to FIG. 2, it can be seen that when a particleenters the detect zone 114, light from the laser beam 104 thatencounters the particle will be scattered, refracted and reflected fromthe particle and a portion of that light may be detected by one or moreof the detectors 112. A particle at a location such as 140 that is notin the detect zone 114, will disperse (scatter, refract and reflect)light, but such light will reach the detectors 112 only at angles fromtheir direction of viewing outside of angle E and will therefore not bedetected by the detectors.

[0072]FIG. 2C illustrates the manner of dispersal of light from thelaser beam 104 when it strikes a small transparent homogeneous sphericalparticle 150. FIG. 2H shows the hypothetical dispersion of a laser beam,by showing variation in detected light intensity as a function of anglefrom the direction of the beam, for a small spherical particle. Theparticle is a polystyrene latex sphere having a diameter ofapproximately 993 nm (0.993 micron), and the laser beam has a wavelengthof approximately 685 nm. FIG. 2H includes two graphs, with one graph 152representing vertically polarized light (for the laser beam and for thedetectors) and graph 154 representing horizontally polarized light. Itis noted that dispersion can change the polarization of the light fornon-spherical particles, and only those components of polarization thatare parallel to the polarization direction of the detector filter 136will pass through and be detected. Because of the symmetrical nature ofa sphere, the sphere 150 of FIG. 2C should uniformly illuminatedetectors that are similarly angled and in the same plane, such asdetectors DN and DP (FIG. 2F). Detectors DM and DP lie in a differentplane and may be differently illuminated due to polarization effects.

[0073]FIG. 2F is an isometric view of the detection system 100 thatincludes two rings 170, 172 resulting in four quadrants 181, 182, 183,184. Sixteen detectors labeled DA-DP are provided, with four detectorsat each quadrant. In one example, four detectors DA-DD on each quadrantare spaced by an angle A (FIG. 2) of 20° from the laser beam forwarddirection 120, four others DE-DH are spaced an angle B of 50°, fourothers DI-DL are spaced at an angle C of 90° and four others DM-DP arespaced at an angle D of 120° from the forward direction 120.

[0074]FIG. 2D shows a particle 190 of generally “pear” shape, with asmaller, discontinuous sub-region 192 (e.g. a cell nucleus) embeddedwithin the particle 190. The particle 190 will disperse light primarilyto one side and perhaps least to the opposite side, depending on itsorientation. Also, dispersion (scattering, refraction and reflection) byan elongated object tends to change the polarization of the light. FIG.2E illustrates another particle 200 of high refractive index such thatlight does not penetrate far into the particle and generally “ovoid”shape, which has opposite sides 202, 204 that are almost flat (largeradius of curvature). This results in considerable light 206 at one sidebeing directed primarily toward one detector.

[0075] In accordance with the invention, a radiation scattering pattern(RSP) is created for a particular type of particle, such as a particularpathogenic bacterium that might be found in water. First, a large number(usually at least hundreds, occasionally thousands, perhaps as many asmillions) of that type of pathogen particle is introduced to water thatis otherwise free of all particles. Then, the particles are allowed topass through the beam 104, with the frequency of such events increasedby increased speed of the fluid. The output signals of each of thesixteen detectors are stored each time light from the laser is detected,which indicates that a particle has entered the detect zone (an event).In one example, perhaps one hundred particles are detected every minute.

[0076] In FIG. 2G, one group 210 of boxes holds a memory that representsnumerous detections of a known specie of a particle, such as a pathogen.The group of boxes includes many rows, with each row containing sixteenboxes DA-DP that represents the output of each of the sixteen detectors(a sub-pattern) each time a particle is detected in the detect zone.Although only twenty rows are shown, an analyzing circuit preferablycontains many more (e.g. hundreds or thousands). The data stored ingroup 210 represents the basis for the RSP for that particular pathogen.Each row of 16 outputs represents the dispersion characteristics foreach of different variations of a species (e.g. that vary slightly insize and shape), and for each of numerous different orientations withrespect to the laser beam 104 for each variation of the species.

[0077] This data is then processed by subjecting the data as a whole toan algorithm, such as MANOVA, which enhances the separation of datagenerated from these particle-type measurements from data generated frommeasurements of distinct particles, and further by subjecting the dataobtained from the algorithm to a mathematical technique that eliminatesthe data generated from each particle unless the particle is of the sametype as the N particles whose generated data is most similar to thatparticle's data, where N is a whole number greater than 0. The resultingdata is the RSP for that particle type.

[0078] The system is then used to identify unknown pathogens that mayhave contaminated water, such as a drinking water supply of a city. Thesame apparatus 100 of FIG. 2F is used to interrogate a sample of thewater (typically a continuous stream) and the laser is continuallyenergized to interrogate the unknown particles. Every time an unknownparticle enters the detect zone 114, resulting in light being detectingby a plurality of detectors, the outputs of the sixteen detectors arerecorded to produce an unknown sub-pattern. The data is then processedin the same manner as the data used to generate the particle typespecific RSPs. In effect, the circuit which records the output of thedetectors for an unknown particle, determines if the sub-pattern in eachrow of memory 212 for one unknown particle, matches one of the numeroussub-patterns in memory 210 for a known particle species. This matchingis repeated for each of several other known pathogen species whosepattern has been recorded in another memory similar to memory 210.

[0079] In FIG. 2G, memory 210 represents the many sub-patterns (sixteennumbers which are the outputs of the sixteen detectors DA-DP when oneparticle is interrogated) recorded for a particular known specie. Anexample is the particle 200 of FIG. 2E. As mentioned above, the particle200 has a flattened surface portion 202 which may reflect considerablelight to one of the four detectors DA-DD which are each angled 20° fromthe laser beam forward direction. For memory 210, a loop is depicted at224 to point out data where only one of the detectors DA-DD has recordeda high light level (it varies between 1 and 16, with 16 being maximumlevel), which may indicate a particle with a flattened surface. Theentire sub-pattern 226 includes sixteen values.

[0080] For the recordings in memory 212 of FIG. 2G, a loop is depictedaround a group of numbers at 234 of the sub-pattern 236 that appear tohave the same characteristics. The sub-patterns 226, 236 of the knownand unknown particles have other characteristics in common, such as ahigh (but decreasing) number in all detectors DD, DI, DL, DP locatedalong one sector, and moderate numbers in detectors DB, DF, DJ, DN in anopposite sector. If many of such characteristics are detected for anunknown particle sub-pattern in memory 212, which closely correspond toone or more sub-pattern for the known specie of particle in memory 210,then this indicates that the unknown particle closely matches the knownparticle type and that the unknown particle is presumptively of the typeof pathogen that has the RSP present for the particular known particletype.

[0081] In accordance with the invention, the RSPs for many differenttypes of known pathogens that are likely to be encountered in a citywater supply are recorded, and used to determine which, if any, of thosemany known pathogens closely corresponds to the unknown particle. Theoutput of the comparison can be a signal that indicates the degree ofcorrelation of the unknown particle with the closest one of the knownparticles. The output can indicate that the unknown particle is of acertain type when there is a high correlation. Although a circuit couldbe utilized that compares known data (for a known pathogen) with newdata (for an unknown pathogen), the present invention can use existingcircuitry which searches for patterns in two groups of data (thesub-pattern of an unknown particle with each of the many sub-patterns ofa known particle), or correlation between the two groups of data, todetermine whether there are very close patterns.

[0082] It is noted that in a city water supply, one might expect todetect (interrogate) many particles that are not pathogens, beforedetecting a particle that is a pathogen and whose sub-pattern closelymatches one of the known-particle patterns that are stored in theknown-particle memories. After a large number of particles are detectedand at least several of hundreds of particles are detected that closelymatch the known-particle pattern for one particular species, one canbegin to be confident that the particular known-particle species ispresent in the city water supply.

[0083] Experimental Procedure

[0084] In the experimental disclosure which follows, all weights aregiven in grams (g), milligrams (mg), micrograms (μg), nanograms (ng), orpicograms (pg), all amounts are given in moles, millimoles (mmol),micromoles (pmol), nanomoles (nmol), picomoles (pmol), or femtomoles(fmol), all concentrations are given as percent by volume (%),proportion by volume (v:v), molar (M), millimolar (mM), micromolar (μM),nanomolar (nM), picomolar (pM), femtomolar (fM), or normal (N), allvolumes are given in liters (L), milliliters (mL), or microliters (μL),power ratings are given in milliwatts (mW), and linear measurements aregiven in millimeters (mm), micrometers (μm), or nanometers (nm), unlessotherwise indicated.

[0085] Cryptosporidium, Giardia, and Algae Samples

[0086] Samples of the two pathogenic microorganisms to be evaluatedusing the present invention, Cryptosporidium parvum oocysts and Giardialamblia cysts, and the background interference particle, an algaespecies Oocystis minuta, are provided by Dr. Jennifer Clancy of ClancyEnvironmental Consultants. Each of the samples has a concentration ofapproximately 10⁷ cells/mL. Microscopic examination of each of thesamples precedes the measurements in order to verify the concentrationof the samples, to reject samples that had significant clumpinggenerally due to age, and to qualitatively observe the individualorganisms to improve consistency of samples.

[0087] Sample Preparation

[0088] The water into which the Laser Troller is submerged is very lowparticulate water, to provide a clean background or “blank.” This lowparticulate water is produced by passing standard tap water through awater conditioner, a carbon filter to eliminate chlorine, a single passreverse osmosis unit, and finally through a series of filters withdecreasing porosity: 200 nm, 100 nm and 50 nm. This produces water withless than about 10 particles of size 200 nm or larger per cubiccentimeter.

[0089] The Laser Troller unit is submerged into a large glass testvessel containing about 60L of very low particulate water. Prior tointroducing target particles into the test vessel, a “blank” measurementis taken to ensure that the water is clean and generally withoutextraneous particles. Such extraneous particles may enter the waterthrough a number of means, including dust particles, particles from thesurface of the Laser Troller, or contamination of the low particulatewater from algae growth.

[0090] A concentrated sample of the target particles (approximately 10⁷particles per mL) is then pipetted into the test vessel. The finalconcentration of target particles is about 200 target particles/mL.Then, the water is agitated by a magnetic stir bar to create a waterspeed of about 1 cm/sec at the laser beam. This particle concentrationand flow speed produces about 100 to 1000 events per hour. Multipleparticle events create potential interference in the single particledetection, are easily identifiable, and are screened out of the data.

[0091] Measurement Apparatus

[0092] Light scattering measurements are performed using an openring-shaped structure, colloquially termed the “Laser Troller,” (P. J.Wyatt and C. Jackson, Limnology and Oceanography, January 1989, pp.96-112), as depicted schematically in FIG. 2F. The Laser Troller uses a25 mW solid state, linearly polarized laser (685 nm wavelength) as itslight source and fiber optic cables to transfer light signal todetectors such as photomultipliers or photodiodes. These cables are heldin place by a set of circular rings that position the 16 fiber opticcable ends on a sphere at predetermined angles surrounding thescattering region, with the ends of the fibers about 6cm from thescattering center of the laser beam. The cable ends each have opticsthat provide analyzers and restrict the field of view of the fibers toabout 2.5° full angle.

[0093] The Laser Troller assembly is completely submerged in water.Prior to making measurements, the Laser Troller is calibrated, and gainsof each of the 16 electro-optical channels are normalized using asolution of dextran particles. Particle measurements are performed byintroducing target particles into the water and stirring the water witha magnetic stir bar such that the target particles pass through thelaser beam and scatter light. The scattered light signals 300 (FIG. 4)are captured by the fiber optic cables 301 and transferred tophotomultiplier tubes (PMTs) 302 that amplify and convert the opticalscattering amplitude into an analog electrical signal. The analog outputof the Laser Troller is transmitted to an analog-digital conversionprinted circuit board 303, where the analog signals are digitized. Thesedigital signals are then transferred to a local central processing unit(CPU) 304, where the signals are analyzed.

[0094] Generation of the Radiation Scattering Pattern

[0095] Samples are individually pipetted into the test vessel, andmeasurements are made over two hours. This procedure generates 841Cryptosporidium measurements, 782 Giardia measurements, and 798 algaemeasurements.

[0096] The maximum values for each channel during a measurement event iscorrected by background subtraction and normalized, and multipleparticle events are rejected (approximately 1% of the events collectedare multiple events). Data are collected by the 16 detectors into 16independent channels, each channel representing an individual scatteringangle. The detectors span the range between 20° and 120° from theforward scattering angle. The maximum values for each of the channelsduring an event are stored in the local CPU.

[0097] Once the data is collected for each of the samples,Cryptosporidium, Giardia, and algae, a subset of the total data is usedto generate the radiation scattering pattern. 580 total measurements ofeach of the microorganisms are filtered by the analysis process,ultimately resulting in an radiation scattering pattern consisting of242 Cryptosporidium measurements and 90 Giardia measurements. Theremainder of the measurements are used to test the effectiveness of thegeneration of the radiation scattering pattern. Representative examplesof raw data are shown in FIGS. 5, 6 and 7.

[0098] The particle type-specific radiation scattering pattern isproduced as follows: The logarithms of the data maxima are taken foreach of the measurements. This modified data is then submitted to astandard statistical algorithm, called multiple analysis of variance(MANOVA). The routine used is part of a statistical package provided byMATLAB (The MathWorks). MANOVA automatically finds the linear space, asa function of the original 16 channels, in which the variance amongsttargets of the same type is minimized and the variance between differenttargets is maximized. This linear space (the canonical space) is, ingeneral, of lower dimensionality than the original space. For the datapresented here, the original 16-dimension space is reduced to threedimensions. The results for the data, prior to applying the techniquedescribed above as the “Wall,” for the Cryptosporidium oocysts, Giardiacysts, and algae are shown in a two dimensional projection in FIG. 8.

[0099] In general, due to variations among the particles of the sametype, as well as variations in background scattering, impure samples,and the like, there will be significant variation in the radiationscattering patterns within a target group. These variations createoverlap in the MANOVA results such that the groups, in general, will notbe completely distinct (this effect is shown clearly in FIG. 8). Rather,the data will preferentially occupy one region in canonical space overothers. However, this preferential region may not be sufficientlydistinct to identify the targets from each other and the backgroundparticles with a high degree of confidence. A further processing stepmay then be utilized.

[0100] For each data point (which includes the target particles ofinterest and all of the background particles), a ranking of the nearestneighbors in canonical space is made. For these measurements, if the 15nearest neighbors are of the same type, then that data point isretained. If any of the 15 nearest neighbors is of another type, thenthat data point is rejected. This technique creates a barrier betweenthe points of one particle type in the canonical space and other types,and thereby generates a unique radiation scattering patterns for eachparticle type in the MANOVA canonical space. It should be noted that allof the algae (the interference particle) data have been removed usingthis algorithm, leaving Cryptosporidium measurements and Giardiameasurements that are fully separated and distinct. It is thiscollection that defines the radiation scattering pattern ofCryptosporidium and Giardia in the MANOVA canonical space.

[0101] The Cryptosporidium and Giardia RSPs can then be further refinedas follows: For each point within a given microorganism's radiationscattering patterns, the average distance between this point and therest of the points in the radiation scattering pattern is calculated.Thus the distribution of “average distances” for all of the points inthe radiation scattering pattern can be calculated. Any individual pointthat has an average distance more than two standard deviations than thegroup as a whole is rejected as an “outlier” and not included in therefined radiation scattering pattern. FIG. 10 shows the result ofapplying such the previously described steps, leaving a total of 242Cryptosporidium measurements and 90 Giardia measurements in the RSP.

[0102] The RSP data, which consists of the positions in canonical dataspace of the data points, shown for example in FIG. 10, and the matrixof MANOVA coefficients is stored in a computer storage medium, such as apersonal computer. The average distance between points in this refinedradiation scattering pattern is calculated for each target particlegroup and used in the identification step, outlined in the following.

[0103] Identification

[0104] Measurements of Cryptosporidium oocysts, Giardia cysts, andalgae, taken from the same physical samples as those used to generatethe RSP, are analyzed as follows in order to demonstrate the ability ofthe present invention to identify these microorganisms and distinguishthem from each other under controlled conditions.

[0105] A 16-channel scattering intensity matrix, derived from the maximaof each of the channels during the time an “unknown” particle (chosenfrom the Cryptosporidium, Giardia, or algae measurements) passed throughthe laser beam, is generated. This 16-channel measurement data is thencorrected for background and normalized. As in the generation of the RSPdescribed above, the logarithm of each of the 16 scattering intensitiesis taken. These intensities are then transformed into the threedimensional MANOVA canonical space, where their position in this spaceis compared to the previously generated type-specific radiationscattering patterns for Cryptosporidium and Giardia.

[0106] The unknown particle's position in canonical space is thencompared to each of the target particle positions to determine if theunknown particle belongs to any of the target particle groups. This isperformed by taking the distance, in canonical space, between theunknown particle and the three nearest particles of a target group. Forexample, if the total distance between the unknown particle and thethree nearest Cryptosporidium points is less than 4.2 times the averagedistance between the Cryptosporidium data points, then the particle isidentified as Cryptosporidium. If the total distance between the unknownparticle and the three nearest Giardia points is less than 1.74 timesthe average distance between the Giardia data points, then the particleis identified as Giardia. These specific threshold values and the numberof nearest particles are optimized empirically to minimize falsepositive identifications, that is, misidentifying algae as eitherCryptosporidium or Giardia.

[0107] Results

[0108] An experimental test set of 580 each of Cryptosporidium, Giardia,and algae measurements are tested using the RSPs developed forCryptosporidium and Giardia. Three criteria are used to quantify theperformance of the identification:

[0109] The “Identification Rate,” which is the ratio of the number ofeach species that are correctly identified relative to the total numberof each that is measured.

[0110] The “Identification Confidence,” which is the probability that apositive identification of Cryptosporidium or Giardia is correct.

[0111] The “False Positive Rate,” which is the rate at which algaebackground is mistakenly identified as either Cryptosporidium orGiardia.

[0112] A measurement treated as an unknown is either identifiedpositively as Cryptosporidium or Giardia, or is rejected as “unknown”.Cryptosporidium Giardia Identification Rate  41.7%   15.5% Identification Confidence 99.31% 99.66% False Positive Rate 0% (<0.17%)0% (<0.17%)

[0113] These results reflect the analysis of blind measurements comparedto the RSP for a single pass through the present detection system. Thisprocedure produced a compact data space for each type of microorganismwithin which there is little doubt to what type a particular data pointbelongs. The application of the “Wall” technique, however, reduces theyield in the operation of the apparatus, i.e. when the present inventiondecides the decision is most likely correct. At this point, in manycases the system would not make any decision at all. However, it can bedemonstrated that repetitions of the measurement will decrease the riskthat a specimen will escape detection. Such repetitions can be arrangedin space and/or in time. It is true that an incorrect arrangement candeteriorate the error probability severely. However, if the falsepositive rate is sufficiently low, as it is in this case, and if thenumber of repetition of measurements is larger than a certain number,both the yield will increase and the error rate will be reduced. Theoptimal number of repetitions is a mathematical function of the yieldachieved by a single measurement.

[0114] For the above results, it is easily shown that seven measurementsof the Giardia and three of the Cryptosporidium increases theIdentification Rate to greater than 70%, far beyond current state of theart.

[0115] All patents and patent applications cited in this specificationare hereby incorporated by reference as if they had been specificallyand individually indicated to be incorporated by reference.

[0116] Although the foregoing invention has been described in somedetail by way of illustration and example for purposes of clarity andunderstanding, it will be apparent to those of ordinary skill in the artin light of the disclosure that certain changes and modifications may bemade thereto without departing from the spirit or scope of the appendedclaims.

1. A method for the identification of unknown particles contained in afluid comprising: a) providing a source of radiation and at least onedetection means to detect said radiation located in a predeterminedposition relative to the radiation source, positioned to investigate afluid, b) interrogating said fluid with said source of radiation; c)measuring the radiation scattered by an unknown particle in the fluid atsaid at least one detection means; d) comparing the results obtained instep (c) with standard results previously obtained from a previouslyidentified particle, wherein said standard results are obtained bygenerating a radiation scattering pattern capable of uniquelyidentifying said previously identified particle; and e) identifying saidunknown particle based upon the comparison of step (d).
 2. The method ofclaim 1 wherein the radiation scattering pattern capable of uniquelyidentifying said previously identified particle was generated bysubjecting measurements of the radiation scattered by said previouslyidentified particle in a fluid to an algorithm which enhances theseparation of data generated from said measurements from data generatedfrom measurements of distinct particles.
 3. The method of claim 2wherein said algorithm is multiple analysis of variance.
 4. The methodof claim 2 further comprising subjecting the data obtained from saidalgorithm to a mathematical technique that further enhances theseparation of data generated from said measurements from data generatedfrom said measurements of distinct particles.
 5. The method of claim 4wherein said mathematical technique eliminates the data generated from aselected, previously identified particle unless the selected, previouslyidentified particle is of the same type as the N particles whosegenerated data is most similar to the selected, previously identifiedparticle's data, where N is a whole number greater than
 0. 6. The methodof claim 1 wherein the source of radiation is a source ofelectromagnetic radiation.
 7. The method of claim 6 wherein the sourceof electromagnetic radiation is a laser.
 8. The method of claim 1wherein the detection means comprises a plurality of separate detectorsarranged in predetermined positions relative to the radiation source. 9.The method of claim 6 wherein the detection means comprises at least onephotodetector and a computer operatively connected to saidphotodetector.
 10. The method of claim 1 wherein the fluid is liquidwater.
 11. The method of claim 1 wherein the particle is amicroorganism.
 12. The method of claim 11 wherein the microorganism is amember selected from the group consisting of Cryptosporidium spp. andGiardia spp.
 13. A method for the identification of unknown particlescontained in a fluid comprising: a) providing a source of radiation andat least one detection means to detect said radiation located in apredetermined position relative to the radiation source, positioned toinvestigate a fluid b) interrogating said fluid with said source ofradiation; c) measuring the radiation scattered by an unknown particlein the fluid at said at least one detection means; d) comparing theresults obtained in step (c) with standard results previously obtainedfrom a previously identified particle, wherein said standard results areobtained by i) generating a radiation scattering pattern capable ofuniquely identifying said previously identified particle by subjectingmeasurements of the radiation scattered by said previously identifiedparticle in a fluid to an algorithm which enhances the separation ofdata generated from said measurements from data generated frommeasurements of distinct particles; and e) identifying said unknownparticle based upon the comparison of step (d).
 14. The method of claim13 wherein said algorithm is multiple analysis of variance.
 15. A methodfor the identification of unknown particles contained in a fluidcomprising: a) providing a source of radiation and at least onedetection means to detect said radiation located in a predeterminedposition relative to the radiation source, positioned to investigate afluid; b) interrogating said fluid with said source of radiation; c)measuring the radiation scattered by an unknown particle in the fluid atsaid at least one detection means; d) comparing the results obtained instep (c) with standard results previously obtained from a previouslyidentified particle, wherein said standard results are obtained by i)generating a radiation scattering pattern capable of uniquelyidentifying said previously identified particle by subjectingmeasurements of the radiation scattered by said previously identifiedparticle in a fluid to an algorithm which enhances the separation ofdata generated from said measurements from data generated frommeasurements of distinct particles, and ii) subjecting the data obtainedfrom said algorithm to a mathematical technique that eliminates the datagenerated from a selected, previously identified particle unless theselected, previously identified particle is of the same type as the Nparticles whose generated data is most similar to the selected,previously identified particle's data, where N is a whole number greaterthan 0; and e) identifying said unknown particle based upon thecomparison of step (d).
 16. A method for the identification of unknownparticles contained in a fluid to be analyzed, wherein such analysisincludes providing a source of radiation and at least one detectionmeans having a plurality of separate detectors to detect said radiationlocated in a predetermined position relative to the radiation source,interrogating the fluid with the source of radiation, and measuring theradiation scattered by an unknown particle in the fluid by saiddetection means, the improvement comprising comparing the resultsobtained by said measurement step with standard results previouslyobtained from a previously identified particle, wherein said standardresults are obtained by generating a radiation scattering patterncapable of uniquely identifying said previously identified particle, andidentifying said unknown particle based upon the comparison step. 17.The method of claim 16 wherein the radiation scattering pattern capableof uniquely identifying said previously identified particle wasgenerated by subjecting measurements of the radiation scattered by saidpreviously identified particle in a fluid to an algorithm which enhancesthe separation of data generated from said measurements from datagenerated from measurements of distinct particles.
 18. The method ofclaim 17 wherein said algorithm is multiple analysis of variance. 19.The method of claim 17 further comprising subjecting the data obtainedfrom said algorithm to a mathematical technique that further enhancesthe separation of data generated from said measurements from datagenerated from said measurements of distinct particles.
 20. The methodof claim 19 wherein said mathematical technique eliminates the datagenerated from a selected, previously identified particle unless theselected, previously identified particle is of the same type as the Nparticles whose generated data is most similar to the selected,previously identified particle's data, where N is a whole number greaterthan
 0. 21. The method of claim 16 wherein the source of radiation is asource of electromagnetic radiation.
 22. The method of claim 21 whereinthe source of electromagnetic radiation is a laser.
 23. The method ofclaim 16 wherein the detection means comprises a plurality of separatedetectors arranged in predetermined positions relative to the radiationsource.
 24. The method of claim 21 wherein the detection means comprisesat least one photodetector and a computer operatively connected to saidphotodetector.
 25. The method of claim 16 wherein the fluid is liquidwater.
 26. The method of claim 16 wherein the particle is amicroorganism.
 27. The method of claim 26 wherein the microorganism is amember selected from the group consisting of Cryptosporidium spp. andGiardia spp.
 28. Apparatus for the identification of unknown particlescontained in a fluid to be analyzed which includes a source of radiationfor generating a radiation beam and at least one detection means havinga plurality of separate detectors to detect said radiation located in apredetermined position relative to the radiation source, such that aparticle intersecting the radiation beam will scatter radiationdetectable by the detectors, and means for measuring the radiationscattered by an unknown particle in the fluid by said detection means,the improvement comprising means for comparing the results obtained bysaid measurement step with standard results previously obtained from apreviously identified particle, wherein said standard results areobtained by generating a radiation scattering pattern capable ofuniquely identifying said previously identified particle, andidentifying said unknown particle based upon the comparison step. 29.The apparatus of claim 28 wherein the radiation scattering patterncapable of uniquely identifying said previously identified particle wasgenerated by subjecting measurements of the radiation scattered by saidpreviously identified particle in a fluid to an algorithm which enhancesthe separation of data generated from said measurements from datagenerated from measurements of distinct particles.
 30. The apparatus ofclaim 29 wherein said algorithm is multiple analysis of variance. 31.The apparatus of claim 29 wherein the radiation scattering patterncapable of uniquely identifying said previously identified particle wasgenerated by further subjecting the data obtained from said algorithm toa mathematical technique that further enhances the separation of datagenerated from said measurements from data generated from saidmeasurements of distinct particles.
 32. The apparatus of claim 31wherein said mathematical technique eliminates the data generated from aselected, previously identified particle unless the selected, previouslyidentified particle is of the same type as the N particles whosegenerated data is most similar to the selected, previously identifiedparticle's data, where N is a whole number greater than
 0. 33. Theapparatus of claim 28 wherein the source of radiation is a source ofelectromagnetic radiation.
 34. The apparatus of claim 33 wherein thesource of electromagnetic radiation is a laser.
 35. The apparatus ofclaim 34 wherein the detection means comprises a plurality ofphotodetectors and a computer operatively connected to saidphotodetectors.